In response to the development of computers that can process increasingly larger amounts of data, encyclopedias, dictionaries, and other content data applications have been implemented in electronic form. Such content data applications make it possible to compile and make available vast amounts of information. However to be useful, the data must be searchable. More recent developments include the implementation of such data applications in a network environment, such as over the Internet. Typically, network implementations can require significant system resources (e.g., computer memory and processor time) to effectively process search queries.
One example of a data content application is the “ENCARTA” brand Multimedia Encyclopedia Application developed and marketed by Microsoft Corporation of Redmond, Wash. The “ENCARTA” brand Multimedia Encyclopedia Application can be run as a stand-alone application on an individual computer or can be operated over a network, such as the Internet. Electronic encyclopedias typically have a massive content data volume that includes all of the articles and other media necessary to render an electronic version of an encyclopedia.
However, to be efficiently used data content applications must be able to process search queries effectively and quickly. As the amount of content increases, the need for more speed increases. Various prior art systems have been developed to speed up content data searching. One of the most common methods of speeding data searching is to use partial data searching. This method speeds data searching by designating only a subset of the entire body of data as searchable. Another known method is to associate searchable key words with an un-searchable body of text data, whereby a search query is processed only against the key words and a match results in returning a reference to the un-searchable body of text data. Neither of these methods is completely satisfactory, because it is impossible to fully predict what search terms a user will select to query a particular body of text data. Consequently, match results are likely to be less than comprehensive.
Obviously, full content data searching is better, but it is typically cost prohibitive in prior art systems, because of the demands on system resources. Therefore, there is a need in the art for an efficient full content data searching technique. The technique should work with disparate content data sources and disparate content data types. The technique also should minimize search times by utilizing a build process to pre-process the full content data to streamline searching during run-time operation. The technique also should support natural word search queries and should use alternative search words and word pairs to increase the accuracy of search results and search process speed.